Mapping and Monitoring of Geohazards with Remote Sensing Technologies

Earth observation (EO) techniques have proven to be reliable and accurate for monitoring land-surface deformations that occur naturally (landslides, earthquakes, and volcanoes) or due to anthropogenic activities (ground-water overexploitation, extraction of oil and gas). In cases where mitigation me...

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Other Authors: Loupasakis, Constantinos (Editor), Papoutsis, Ioannis (Editor), Nikolakopoulos, Konstantinos G. (Editor)
Format: Electronic Book Chapter
Language:English
Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2023
Subjects:
Online Access:DOAB: download the publication
DOAB: description of the publication
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520 |a Earth observation (EO) techniques have proven to be reliable and accurate for monitoring land-surface deformations that occur naturally (landslides, earthquakes, and volcanoes) or due to anthropogenic activities (ground-water overexploitation, extraction of oil and gas). In cases where mitigation methods have to be put into practice, the detailed mapping, characterization, monitoring and simulation of the geocatastrophic phenomena have to precede their design and implementation. EO techniques possess high potential and suitability as alternative, cost-efficient methods for the management of geohazards, and have been proven to be a valuable tool for verifying and validating the spatial extent and evolution of the deformations. To this extent, the current reprint covers innovative applications and case studies on the mapping and monitoring of all kinds of geohazards with remote-sensing technologies. Submissions that make use of new tools and methodologies, including the use of data-driven machine learning methods, were encouraged. 
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650 7 |a Technology: general issues  |2 bicssc 
650 7 |a History of engineering & technology  |2 bicssc 
653 |a Geohazards 
653 |a InSAR 
653 |a Remote sensing 
653 |a Photogrammetry 
653 |a Unmanned aerial vehicles 
653 |a GNSS 
653 |a TLS 
653 |a Persistent scatterer interferometry 
653 |a Machine learning 
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856 4 0 |a www.oapen.org  |u https://directory.doabooks.org/handle/20.500.12854/128669  |7 0  |z DOAB: description of the publication